Evaluate the Effectiveness of Leveraging Loan Advisory Service with AI-Powered Chatbot Technology in the Sri Lankan Context

Abstract

This study aims to test the efficiency of the use of chatbot technology with AI features in offering loan advisory services in Sri Lanka using the Elaboration Likelihood Model (ELM) combined with the Trust theory. The study examines the influence of Message Level (ML), Authenticity Level (AL), and Quality Level (QL) on users' intention to use AI-driven financial advisory services. A sample of 243 millennials and Generation Z participants in Colombo used a custom-developed loan advisory chatbot prototype connected to the personal loan data of 24 Sri Lankan banks. The analysis of the data with SPSS and SmartPLS 4 indicated that two-sided message recommendation messages greatly benefited ML, AL, and QL, with ML and QL positively affecting the user intention. Nevertheless, it was found that Trust did not significantly moderate the willingness to get AI-based loan advice, which implies that trust itself would not have a significant effect. The results show that a quality message, a credible and well-structured message, influences adoption intention more than trust-based elements. The study contributes to the body of knowledge about AI chatbots' effectiveness in new markets of providing financial services and what can be seen as practical guidelines when developing an AI-driven advisory service to suit the preferences and decision-making behavior of Sri Lankan consumers.

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